Method and system for generating a robotic program for industrial coating

ABSTRACT

Systems and a method predict a generation of a robotic program for industrial coating. Inputs are received including a virtual representation of a robot, a coating gun, elements of the object surface to be coated and a set of desired coating thickness ranges. Inputs on a coating dispersion object are also received. Training data of a plurality of robotic programs for industrial coating and of their corresponding coating thickness coverage on a plurality of surfaces are received. The training data are processed in x, y tuples so as to learn a mapping function to generate a coating prediction module. Starting with a given selected valid thickness coverage as input parameters, it is proceeded in an iterative manner to predict a robotic program via the coating prediction module. A coating robotic program is generated for each surface element based on the resulting predicted coating programs.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority, under 35 U.S.C. § 119, of Germanapplication EP 19180630, filed Jun. 17, 2019; the prior application isherewith incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure is directed, in general, to computer-aideddesign, visualization, and manufacturing (“CAD”) systems, productlifecycle management (“PLM”) systems, product data management (“PDM”)systems, and similar systems, that manage data for products and otheritems (collectively, “Product Data Management” systems or PDM systems).More specifically, the disclosure is directed to production environmentsimulation.

The process of applying coating materials on surfaces of objects is acommon operation in a large variety of industrial processes.

As used in the art, the term “coating” may denote either the verb, i.e.the operation of applying coating material, e.g. by spraying, or thenoun, i.e. the coating material itself. In the industrial domain, a“coating material”, or simply a “coating”, is a layer of a substanceapplied over an object surface, for example for protection, functionaland/or decoration purposes.

In addition, coating materials applied to base materials may be used toprovide properties not inherent in the base, including corrosion,wear-resistance, conductivity, color, solderability and others.

Examples of coating materials are paints, lacquers, metal platings,thermal-barrier coating materials, anti-corrosion coating materials andother types of protective, functional or decorative coating materials.

As used herein, the term coating may also denote additive manufacturingwhere a three-dimensional (“3D”) industrial object is built by addinglayer-upon-layer of material, e.g. the coating material.

The amount of coating applied on a product object surface, that is, thecoating thickness, often contributes to the product quality standard.

In fact, for quality, security, environmental, marketing and costefficiency purposes, a coating operation is required to meet certainlevels in terms of the achieved thicknesses and uniformity levels of thecoating layer.

In several industrial processes, coating operations are mostly performedby coating guns which are mounted on robots.

Programming robots for coating activities in industrial cells is anexhausting, iterative, error prone and time-consuming task.

Improved techniques are desirable.

BRIEF SUMMARY OF THE INVENTION

Various disclosed embodiments include methods, systems, and computerreadable mediums for generating a robotic program for industrial coatingwherein the coating material is to be applied on a surface of anindustrial object by a coating gun of a robot. The method includesreceiving inputs including virtual representation of a robot, virtualrepresentation of a robotic coating gun, virtual representation of a setof elements of the object surface to be coated and a set of desiredcoating thickness ranges, virtual representation of an industrialsub-cell. The method includes receiving inputs on a coating dispersionobject to be mounted on the coating gun for emulating coating behaviorof the coating gun. The method further includes receiving training dataof a plurality of robotic programs for industrial coating and theircorresponding coating thickness coverage on a plurality of surfaces. Themethod further includes processing the training data for machinelearning purposes to obtain first data tuples x and second data tuplesy; wherein the x tuples are describing a point sequence on the surface,the corresponding coating thickness coverage and specific information onthe robot and on the surface; wherein the y tuples are describing thecorresponding robotic program. The method further includes learning fromthe processed data a function mapping the x tuples into the y tuples togenerate a coating prediction module for the robot. The method furtherincludes, for a given point sequence of each given surface element,proceeding, starting with a given selected valid thickness coverage asinput parameters, in an iterative manner to:

a) predict a robotic program via the coating prediction module;b) simulate the predicted robotic program with a collision detectionengine within the industrial sub-cell;c) calculate the thickness values of the coating material on the givensurface element by detected collisions between elements of the coatingdispersion object mounted on the used robotic coating gun andsub-elements of the given surface element; andd) where the input parameters are iteratively tuned until the calculatedthickness values correspond to the set of desired value range and thereare no unallowed robotic collisions within the industrial sub-cell. Themethod further includes generating a coating robotic program for eachsurface element based on one or more resulting tuned robotic programs.

The foregoing has outlined rather broadly the features and technicaladvantages of the present disclosure so that those skilled in the artmay better understand the detailed description that follows. Additionalfeatures and advantages of the disclosure will be described hereinafterthat form the subject of the claims. Those skilled in the art willappreciate that they may readily use the conception and the specificembodiment disclosed as a basis for modifying or designing otherstructures for carrying out the same purposes of the present disclosure.Those skilled in the art will also realize that such equivalentconstructions do not depart from the spirit and scope of the disclosurein its broadest form.

Before undertaking the detailed description below, it may beadvantageous to set forth definitions of certain words or phrases usedthroughout this patent document: the terms “include” and “comprise,” aswell as derivatives thereof, mean inclusion without limitation; the term“or” is inclusive, meaning and/or; the phrases “associated with” and“associated therewith,” as well as derivatives thereof, may mean toinclude, be included within, interconnect with, contain, be containedwithin, connect to or with, couple to or with, be communicable with,cooperate with, interleave, juxtapose, be proximate to, be bound to orwith, have, have a property of, or the like; and the term “controller”means any device, system or part thereof that controls at least oneoperation, whether such a device is implemented in hardware, firmware,software or some combination of at least two of the same. It should benoted that the functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely.Definitions for certain words and phrases are provided throughout thispatent document, and those of ordinary skill in the art will understandthat such definitions apply in many, if not most, instances to prior aswell as future uses of such defined words and phrases. While some termsmay include a wide variety of embodiments, the appended claims mayexpressly limit these terms to specific embodiments.

Other features which are considered as characteristic for the inventionare set forth in the appended claims.

Although the invention is illustrated and described herein as embodiedin a method and a system for generating a robotic program for industrialcoating, it is nevertheless not intended to be limited to the detailsshown, since various modifications and structural changes may be madetherein without departing from the spirit of the invention and withinthe scope and range of equivalents of the claims.

The construction and method of operation of the invention, however,together with additional objects and advantages thereof will be bestunderstood from the following description of specific embodiments whenread in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a block diagram of a data processing system in which anembodiment can be implemented;

FIG. 2 is a perspective view of an industrial robot with a coatingdispersion object mounted on its coating gun nozzle for generating acoating robotic program in accordance with disclosed embodiments;

FIG. 3 is a perspective view of possible different examples of coatingguns to be mounted on a robotic head;

FIG. 4 is a schematic view of a coating dispersion object 407 inaccordance with disclosed embodiments;

FIG. 5 is a schematic view of a part surface with surface elements501-510 and corresponding desired coating thickness ranges in accordancewith a disclosed embodiment;

FIG. 6 illustrates a schematic view of a part surface 600 with surfaceelements and corresponding desired coating thickness ranges inaccordance with another disclosed embodiment;

FIG. 7 is a schematic view of a part surface from FIG. 6 where tensurface elements have been considered for generating a coating roboticprogram according to the disclosed embodiments;

FIG. 8A-C are schematic views showing valid and non-valid thicknesscoverages on surfaces according to the disclosed embodiments; and

FIG. 9 is a flowchart for generating a robotic program for industrialcoating in accordance with disclosed embodiments.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1 through 9, discussed below, and the various embodiments used todescribe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any suitably arranged device. The numerous innovativeteachings of the present application will be described with reference toexemplary non-limiting embodiments.

Previous techniques for generating a robot program for industrialcoating have some drawbacks. The embodiments disclosed herein providenumerous technical benefits, included but not limited to the followingexamples.

Embodiments enable automatic generation of a collision-free coatingrobotic program with the desired coating thickness coverage.

Embodiments enable reaching a faster, accurate and reliable solution tothe coating industry quality requirements with reduced complexity, costsand efforts.

Referring now to the figures of the drawings in detail and first,particularly to FIG. 1 thereof, there is shown a block diagram of a dataprocessing system 100 in which an embodiment can be implemented, forexample as a PDM system particularly configured by software or otherwiseto perform the processes as described herein, and in particular as eachone of a plurality of interconnected and communicating systems asdescribed herein. The data processing system 100 illustrated can includea processor 102 connected to a level two cache/bridge 104, which isconnected in turn to a local system bus 106. The local system bus 106may be, for example, a peripheral component interconnect (PCI)architecture bus. Also connected to the local system bus in theillustrated example are a main memory 108 and a graphics adapter 110.The graphics adapter 110 may be connected to a display 111.

Other peripherals, such as local area network (LAN)/Wide AreaNetwork/Wireless (e.g. WiFi) adapter 112, may also be connected to localsystem bus 106. An expansion bus interface 114 connects the local systembus 106 to the input/output (I/O) bus 116. The I/O bus 116 is connectedto a keyboard/mouse adapter 118, a disk controller 120, and an I/Oadapter 122. The disk controller 120 can be connected to a storage 126,which can be any suitable machine usable or machine readable storagemedium, including but are not limited to nonvolatile, hard-coded typemediums such as read only memories (ROMs) or erasable, electricallyprogrammable read only memories (EEPROMs), magnetic tape storage, anduser-recordable type mediums such as floppy disks, hard disk drives andcompact disk read only memories (CD-ROMs) or digital versatile disks(DVDs), and other known optical, electrical, or magnetic storagedevices.

Also connected to the I/O bus 116 in the example shown is an audioadapter 124, to which speakers (not shown) may be connected for playingsounds. Keyboard/mouse adapter 118 provides a connection for a pointingdevice (not shown), such as a mouse, trackball, trackpointer,touchscreen, etc.

Those of ordinary skill in the art will appreciate that the hardwareillustrated in FIG. 1 may vary for particular implementations. Forexample, other peripheral devices, such as an optical disk drive and thelike, also may be used in addition or in place of the hardwareillustrated. The illustrated example is provided for the purpose ofexplanation only and is not meant to imply architectural limitationswith respect to the present disclosure.

A data processing system in accordance with an embodiment of the presentdisclosure can include an operating system employing a graphical userinterface. The operating system permits multiple display windows to bepresented in the graphical user interface simultaneously, with eachdisplay window providing an interface to a different application or to adifferent instance of the same application. A cursor in the graphicaluser interface may be manipulated by a user through the pointing device.The position of the cursor may be changed and/or an event, such asclicking a mouse button, generated to actuate a desired response.

One of various commercial operating systems, such as a version ofMicrosoft Windows™, a product of Microsoft Corporation located inRedmond, Wash. may be employed if suitably modified. The operatingsystem is modified or created in accordance with the present disclosureas described.

LAN/WAN/Wireless adapter 112 can be connected to a network 130 (not apart of the data processing system 100), which can be any public orprivate data processing system network or combination of networks, asknown to those of skill in the art, including the Internet. The dataprocessing system 100 can communicate over the network 130 with theserver system 140, which is also not part of the data processing system100, but can be implemented, for example, as a separate data processingsystem 100.

FIG. 2 illustrates a schematic view of an industrial robot with acoating dispersion object mounted on its coating gun nozzle forgenerating a coating robotic program in accordance with disclosedembodiments.

In FIG. 2 it is shown a virtual representation of an industrial sub-cell201 and a virtual representation of an industrial robot 202 with acoating gun 203 applying coating material on a surface 204 of anindustrial object.

With the term “industrial sub-cell” we indicate an industrial cell orany portion of the industrial cell or any 3D environment around therobot 202 which may also include possible obstacles and/or forbiddenvolumes for detecting unallowed collisions with the robot. In theembodiment example of FIG. 2 an obstacle 208 is shown, a collisionbetween the robot 202 and the obstacle 208 is herein denoted as“unallowed” collision. It is noted that the terminology “unallowed”collision is herein used for clarifications purposes to distinguish suchtype of collision from the other types of collisions to be detected by acollision detection engine for coating calculation purposes as discussedin detail in a subsequent section. The “coating” collisions are detectedbetween 3D elements (not shown) of a coating dispersion object 207 andelements 205, 206 of the object surface 204.

Herein, a robotic program is considered to be collision-free when thereare no “unallowed” collisions between the robot 202 and any potentialobstacles 208. The 3D coating dispersion object 207 is composed of aplurality of 3D entities (not shown) each one having an associatedcoating dispersion value for calculating the applied coating thicknessvia the collision detection engine as discussed in detail in asubsequent section.

As shown in FIG. 2, coating material is applied by a coating gun 203 onthe object surface 204. In embodiments, one or more relevant surfaceareas to be coated are selected, and a mesh representation is generatedfor the selected surface areas. A polygon mesh contains a collection ofvertices 205, edges, and surface sub-elements 206 (e.g. usuallytriangles) defining the shape of a polyhedral object in 3D computergraphics. The resulting mesh geometry is like the geometry of thesurface of the original object, and the mesh geometry contains severalmesh sub-elements herein also called mesh-points. As a result, eachsub-element of the mesh is a representation of a sub-element on thepart/object surface. Those skilled in the art will understand that thedensity of the mesh points depends on the desired level of accuracy andthe surface shape. The mesh density may also depend on the used sprayingtechniques; for example, laser paint spraying may require higher meshelement density than, for example, traditional non-laser paint gunspraying techniques.

The coating dispersion object 207 is mounted on the coating gun 203 foremulating coating behavior of the coating gun 203.

The coating dispersion object 207, with its plurality of 3D entities,represents the coating behavior of a given gun and/or of a given brushfor the same given gun for a given coating material having certainphysical properties, e.g. certain density and certain specific gravity.

It is noted that the given coat dispersion object 207 enablescalculating the coating thickness applied on a surface while consideringdifferent mutual positions (e.g. different distances and/or angleswithin certain ranges) between the coating gun and the object surface.However, as the skilled in the art appreciates, for certain variabilityranges of the angle, in order to achieve a higher calculation accuracy,it may be preferable to have a set of different coating dispersionobjects corresponding to a different set of applied angles between thegun and the object surface.

FIG. 3 illustrates possible different examples of coating guns 301 to bemounted on a robotic head. Each coating gun 301 has at least onecorresponding coating dispersion object (not shown) for a given coatingmaterial type. Each coating gun may have several different coatingdispersion objects when several brushes are used and when severalapplied angles above a certain threshold are to be considered.

The coating dispersion object received as input for coating calculationpurposes may be generated with numerous different techniques, includingbut not limited to the following examples: coating dispersion objectsmay be generated departing from a collection of measured real thicknessfootprints of coating material applied on real test surfaces by givencoating guns and/or brushes as disclosed in U.S. patent application Ser.No. 14/809,343, wherein:

a) the coating dispersion objects may be generated with physical modelsfor exact calculation of the applied coating thickness in industrialprocesses;b) the coating dispersion objects may be generated with heuristicfunctions and/or with interpolations techniques;c) in embodiments, the coating dispersion objects may be generated withmachine learning;d) techniques as illustrated with the example embodiment of FIG. 4. Inembodiments, the coating dispersion objects may be generated with anycombination of the above examples.

FIG. 4 illustrates a schematic view of a coating dispersion object 407in accordance with disclosed embodiments. The coating dispersion object407 is composed by a plurality of 3D entities each one with acorresponding coating dispersion value for coating calculation purposes.In FIG. 4 two 3D entities 401, 402 are shown with their corresponding(X,Y,Z) coordinates.

In embodiments, the coating dispersion object 407 may be generated via afunction trained by a machine learning (“ML”) algorithm. The trainingdata are organized in input and output data where the output trainingdata is related to the input training data. In embodiments, the inputtraining data includes robotic gun information (e.g. type of coatinggun, type of brush, index, angle and/or index of the coating dispersionobject when supporting several different coating dispersion objects in asingle ML module) and physical property information of the coatingmaterial and the output training data includes (X,Y,Z) coordinates ofthe 3D entities 401, 402 of the coating dispersion object 407 and theircorresponding coating dispersion values (not shown).

With the above described input and output training data, a correspondingfunction for generating the coating dispersion object may convenientlybe trained.

Thereafter, the model of the coating dispersion object is generated asoutput by applying the trained function to input data for exampleincluding information received on the robotic gun and on the coatingmaterial desired.

FIG. 5 illustrates a schematic view of a part surface with surfaceelements 501-510 and corresponding desired coating thickness ranges inaccordance with a disclosed embodiment.

In FIG. 5 there is shown a representation of a surface 500 of anindustrial object, for example a part of a car over which coating is tobe applied for example by spraying.

A mesh of the surface 500 is created by selecting relevant surfaceelements 501-510 and by creating a mesh (not shown) for each surfaceelement. The mesh geometry is similar to the surface element geometryand is composed of a plurality of mesh elements (e.g. point, edge,vertexes and faces), often called mesh points. As a result, each pointof the mesh represents a point on the corresponding part surface element502 and it is herein denoted as point or sub-element of the surfaceelement.

In FIG. 5 are shown examples of values of desired coating thicknessranges given in mm for each surface element 501-510. For example, forsurface element 501 a desired coating thickness range of 9-10 mm isgiven. In embodiments, upon user selection, the given range of 9-10 mmfor the element 501 may have different meanings and correspondingrequirements. According to a first requirement, the desired coatingthickness may be required to be constant so that any constant valuebetween 9 and 10 mm is considered a valid coverage thickness range.

According to a second requirement, the desired thickness may be anyvariable value between 9 and 10 mm so that for example a thicknesscoverage with some sub-areas with a thickness of 9.1 mm and othersub-areas with a thickness of 9.5 mm is to be considered valid coverage.

In some requirements, a delta value may be given to specify what is theacceptable maximum value difference between points of the same surfaceelement 501 or across different surface elements 501-510. For example,if a selected maximum delta value of 1 mm for elements 501, 502 isgiven, it is acceptable to have points with thickness 9 mm and otherpoints with thickness 10 mm but it is not valid to have points withthickness 9 mm in the first element 501 and points with thickness 11 mmin the second element 501.

In embodiments, the thickness the coating material applied to thesurface 204, 500 is calculated via simulation with a collision detectionengine.

In the 3D virtual environment, the collision set to be detected forthickness calculation purposes is being defined between the followingtwo groups of entities: the mesh points of the part surface 204 and the3D entities of the coating dispersion objects 401, 402.

A robotic program of the coating path operation is simulated via a 3Dvirtual environment software.

The robotic motion planning module for the simulation may be for examplebased on one of the following modules:

a) Realistic Controller Simulation (“RCS”) modules based on RealisticRobot Simulation (“RRS”) interfaces typically provided by robot vendors;b) Virtual Robot Controller (“VRC”) applications of robot vendors;c) an internal programmed motion planning engine like for example theMOP module in Process Simulate of Siemens Tecnomatix suite; andd) a motion planning module obtained via machine learning training asdisclosed in US patent application with application Ser. No. 16/196,156.

During the simulation, the coating dispersion object 207 is mounted ontop of the robot's coating gun 203 for coating calculation purposes sothat while the robot is moving it collides with certain mesh points ofthe part surface 204. For each given time interval, the dynamiccollision detection engine reports which collision pairs are detected(mesh point vs. 3D object entity). For each reported collision pair, atruntime, the thickness value is then calculated with the coatingdispersion value of the 3D object entity involved in the robotcollision. The calculated thickness is then added to the collided meshpoint thickness. Along the simulation, each mesh point holds its totalthickness value so that also the spraying exposure time of the sprayinggun is taken into account. This can for example be represented bythickness color maps, numerical representations or any other desiredrepresentation technique.

It is noted that during simulation of the robot motion, the collisiondetection engine reports also if there are un-allowed collision withobstacles or forbidden volumes (e.g. other pieces of equipment, humansor forbidden zones) in the industrial sub-cell to obtain as result agenerated robotic program which is collision free.

Example Embodiment: Exemplary Structure of Input/Output Data for aMachine Learning Module

In embodiments, the training data may synthetically be generated for aplurality of scenarios. For example, several different types of parts tobe sprayed may be used. For each part, a plurality of robotic programsis generated. A plurality of coating dispersion objects may be used asmodels depending also on the plurality of coating guns/brushes andangles which are used. The coating gun, and the coating dispersionobject are picked and mounted on the robot. Each robot program issimulated, and the corresponding coating thickness coverage iscalculated with the collision detection engine.

The training data is processed to obtain input and output data formachine learning purposes.

For example, in embodiments, for each simulated robotic program, thefollowing information is extracted and organized in input data tuples xand output data tuples y for machine learning purposes:

Input data tuple x contains:

-   -   List of ordered points upon the part surface.    -   For each point, (X,Y,Z) coordinates and coating thickness value.    -   Robot-part mutual information, e.g. delta between the robot base        and the part position, delta between robot tool frame and the        robot Tool    -   Center Point Frame (“TCPF”) and other relevant information.

Output data tuple y contains:

-   -   robotic programs corresponding to the tuple x.

In embodiments, the robotic program of the tuple y may be given as listof list of robotic locations (with X,Y,Z, RX, RY, RZ coordinates) andwhereby, for each location, also the corresponding robotic motioninstructions may preferably be given (e.g. speed, acceleration,configuration, etc.). In the example embodiment of FIG. 7, three roboticlocations 720, 730, 740 are shown for a robotic path during coating of asurface segment consisting of the two surface elements 701, 702.

A coating prediction module is generated by a function trained by amachine learning algorithm with the processed training data by learninga function mapping the x tuples into the y tuple. The machine learningmodule is trained to get “tuple x” as an input and to retrieve back“tuple y” as an output.

The type of this problem is called regression problem and in embodimentsit is solved by using supervised learning algorithms. Supervisedlearning algorithms try to model relationships and dependencies betweenthe target prediction output and the input features such that it ispossible to predict the output values for new data based on thoserelationships learnt from the training data sets.

Numerical Example Embodiment

FIG. 6 illustrates a schematic view of a part surface 600 with surfaceelements 601, 602 and corresponding desired coating thickness ranges inaccordance with another disclosed embodiment.

In FIG. 6, surface 600 has desired coating thickness ranges of 8 mm onthe left surface element 601 and 10-12 mm on the right surface element602. Assume that the allowed delta for the whole surface 600 is selectedto be 1.5 mm for this example embodiment.

FIG. 7 illustrates a schematic view of the part surface of FIG. 6 whereten surface elements 701-710 have been considered for generating acoating robotic program according to disclosed embodiments.

In FIG. 7, the robot moves along robotic locations 720, 730 and 740 tospray a first surface segment formed of the two upper surface elements701, 702. In this example, the surface 600 consists of five segments,whereby a segment consists of two adjacent surface elements 701-702,703-704, . . . , 709-710 which are coated in one robotic path consistingof three consequent robotic locations 720, 730 and 740.

In embodiments, algorithm steps include:

1) picking a coating gun and mounting it on the robot together with thecorresponding coating dispersion object;

2) calculating delta between the robot base and the part position anddelta between robot tool frame and the robot TCPF;

3) creating tuple x for a segment 701-702, 703-704, . . . , 709-710:

-   -   creating a sequence of points (not shown) along the segment;    -   collecting the point sequence coordinates;

4) picking for each point a valid coating thickness value, i.e. validunder the range and delta constraints;

5) inserting the created tuple x data into the trained function moduleand getting back a “tuple y” data results, the predicted robotic programgiven in terms of robotic locations 720, 730, 740 and correspondingrobotic motion instructions;

6) simulating the predicted robotic program and calculating thethickness coverage;

7) if the thicknesses results are in a valid range (See FIG. 9A) and ifthere is no forbidden collision, a robotic program based on thepredicted robotic program is generated for this segment and go to step3) by selecting the subsequent segment or done if all five segments arefinished; and

8) If the thicknesses results are not in a valid range (see FIGS. 9B,9C) or if there is a forbidden collision, go to step 4 by selectingdifferent valid coating thickness values range.

FIGS. 8A-8C illustrate schematic views showing valid and non-validthickness coverages on surfaces according to disclosed embodiments.

In FIG. 8A, the simulated coating thickness results are in a valid rangewhile in FIGS. 8B and 8C the simulated coating thickness results are notin a valid range. In particular, for FIG. 8 B the thickness value of 10mm for surface element 701 is outside the desired coverage of 8 mm witha delta of 1.5 mm and for FIG. 8 C the thickness value of 11 mm has adelta above 1.5 mm with respect of the 8 mm of the adjacent surfaceelement.

Another Example Embodiment of Algorithm Steps

A robotic program for industrial coating is generated, for an industrialprocess where coating material is to be applied on a surface of anindustrial object by a coating gun of a robot. The algorithm stepsinclude:

a) receiving inputs including virtual representation of a robot, virtualrepresentation of a robotic coating gun, virtual representation of a setof elements of the object surface to be coated and a set of desiredcoating thickness ranges, virtual representation of an industrialsub-cell;b) receiving inputs on a coating dispersion object to be mounted on thecoating gun for emulating coating behavior of the coating gun;c) receiving training data of a plurality of robotic programs forindustrial coating and of their corresponding coating thickness coverageon a plurality of surfaces;d) processing the training data for machine learning purposes to obtainfirst data tuples x and second data tuples y; wherein the x tuples aredescribing a point sequence on the surface, the corresponding coatingthickness coverage and specific information on the robot and on thesurface; wherein the y tuples are describing the corresponding roboticprogram;e) learning from the processed data a function mapping the x tuples intothe y tuples to generate a coating prediction module for the robot;f) for a given point sequence on a given surface element, with a givenselected valid thickness coverage, predicting the corresponding roboticprogram via the coating prediction module;g) simulating with a collision detection engine the robot's motionwithin the industrial sub-cell with the predicted robotic program;h) calculating the thickness values of the coating material on the givensurface element by detected collisions between elements of the coatingdispersion object mounted on the used robotic coating gun andsub-elements of the given surface element;i) if the calculated thickness values correspond to the set of desiredvalue range and if there are no unallowed robotic collisions within theindustrial sub-cell, go to sub-step I2) otherwise perform a tunediteration by going to sub-step i1);i1) tuning the robot's motion by selecting a different given validthickness coverage for the given point sequence and repeating step F)until I) until end of iterations; andi2) generating the coating robotic program for the given surface elementbased on the predicted robotic program; and if there are other surfaceelements to be coated, selecting another given surface element andrepeating step F) until I).

In embodiments, at least one of the tuned iterations include selecting adifferent coating gun, brush and/or coating angle between the objectsurface and the coating gun. Accordingly, a different correspondingcoating dispersion object is then used during the simulation.Advantageously, the skilled in the art easily appreciates that theoutput of the coating prediction module depends on the coatingdispersion object used for the input training data during the trainingof the ML module. In embodiments, several dispersion objects can beused. According to embodiments, it is possible to use a single ML moduleby inserting the dispersion object index as part of the tuple x so thatthe index is part of the input data tuple x. According to otherembodiments, several ML modules are used one for each coating dispersionobject and for prediction purposes the relevant ML module is used eachtime.

In embodiments, the data describing the robotic locations comprise atleast information on the positions of the location (e.g. poses,positions and/or directions of the location) and, optionally, it mayadditionally comprise information on the robotic motion at the location(e.g. speed, acceleration, tension, state and/or other robotic motionrelated information at the location).

In embodiments, the output data tuple y contains the minimum informationrequired for describing the robotic program of the specific robot.

In embodiments, the information describing the location position may begiven in the form of spatial coordinates describing the robot's tipposition independently on the robot type or it may be given as robotposes (e.g. via robot's joint values). In other embodiments, thelocation position information may be given in other formats that may ormay not be robot specific.

In embodiments, position information of the robotic locations mayconveniently be given as relative to a given reference frame of thespecific robot. Preferably, the given reference frame of the specificrobot may be the robot base frame.

In embodiments, the input data contains robotic tool-relatedinformation. For example, robot tool-related information may compriserobot's tool type and/or tool differential position information. Thedifferential tool position information may preferably be the deltabetween the robot's tool frame and the robot's TCPF frame.

In embodiments, the object surface to be coated is the surface of asupport material for additive manufacturing purposes.

FIG. 9 illustrates a flowchart 900 of a method for generating a roboticprogram for industrial coating in accordance with disclosed embodiments.Such method can be performed, for example, by system 100 of FIG. 1described above, but the “system” in the process below can be anyapparatus configured to perform a process as described.

At step 902, inputs are received including virtual representation of arobot, virtual representation of a robotic coating gun, virtualrepresentation of a set of elements of the object surface to be coatedand a set of desired coating thickness ranges, and virtualrepresentation of an industrial sub-cell.

At step 904, inputs on a coating dispersion object to be mounted on thecoating gun for emulating coating behavior of the coating gun arereceived.

At step 906, training data of a plurality of robotic programs forindustrial coating and of their corresponding coating thickness coverageon a plurality of surfaces are received.

At step 908, the training data are processed for machine learningpurposes to obtain first data tuples x and second data tuples y; whereinthe x tuples are describing a point sequence on the surface, thecorresponding coating thickness coverage and specific information on therobot and on the surface; wherein the y tuples are describing thecorresponding robotic program.

At step 910, from the processed data, it is learnt a function mappingthe x tuples into the y tuples to generate a coating prediction modulefor the robot.

At step 912, for a given point sequence of each given surface element,it is proceeded starting with a given selected valid thickness coverageas input parameters, in an iterative manner to:

-   -   predict a robotic program via the coating prediction module;    -   simulate the predicted robotic program with a collision        detection engine within the industrial sub-cell;    -   calculate the thickness values of the coating material on the        given surface element by detected collisions between elements of        the coating dispersion object mounted on the used robotic        coating gun and sub-elements of the given surface element; where        the input parameters are iteratively tuned until the calculated        thickness values correspond to the set of desired value range        and there are no unallowed robotic collisions within the        industrial sub-cell.

At step 914, it is generated a coating robotic program for each surfaceelement based on one or more predicted robotic programs resulting fromstep 912.

In embodiments, at least one of the tuned iterations include selecting adifferent coating gun, brush and/or angle and by selecting a differentcorresponding coating dispersion object.

In embodiments, inputs on a coating dispersion object generated via amachine learning algorithm are received.

Of course, those of skill in the art will recognize that, unlessspecifically indicated or required by the sequence of operations,certain steps in the processes described above may be omitted, performedconcurrently or sequentially, or performed in a different order.

Those skilled in the art will recognize that, for simplicity andclarity, the full structure and operation of all data processing systemssuitable for use with the present disclosure is not being illustrated ordescribed herein. Instead, only so much of a data processing system asis unique to the present disclosure or necessary for an understanding ofthe present disclosure is illustrated and described. The remainder ofthe construction and operation of data processing system 100 may conformto any of the various current implementations and practices known in theart.

It is important to note that while the disclosure includes a descriptionin the context of a fully functional system, those skilled in the artwill appreciate that at least portions of the present disclosure arecapable of being distributed in the form of instructions containedwithin a machine-usable, computer-usable, or computer-readable medium inany of a variety of forms, and that the present disclosure appliesequally regardless of the particular type of instruction or signalbearing medium or storage medium utilized to actually carry out thedistribution. Examples of machine usable/readable or computerusable/readable mediums include: nonvolatile, hard-coded type mediumssuch as read only memories (ROMs) or erasable, electrically programmableread only memories (EEPROMs), and user-recordable type mediums such asfloppy disks, hard disk drives and compact disk read only memories(CD-ROMs) or digital versatile disks (DVDs).

Although an exemplary embodiment of the present disclosure has beendescribed in detail, those skilled in the art will understand thatvarious changes, substitutions, variations, and improvements disclosedherein may be made without departing from the spirit and scope of thedisclosure in its broadest form.

None of the description in the present application should be read asimplying that any particular element, step, or function is an essentialelement which must be included in the claim scope: the scope of patentedsubject matter is defined only by the allowed claims.

1. A method for generating, via a data processing system, a roboticprogram for industrial coating, wherein a coating material is to beapplied on a surface of an industrial object by a coating gun of arobot, which comprises the following steps of: a) receiving inputsincluding a virtual representation of the robot, a virtualrepresentation of the coating gun, a virtual representation of a set ofelements of the surface to be coated, a set of desired coating thicknessranges, and a virtual representation of an industrial sub-cell; b)receiving inputs on a coating dispersion object to be mounted on thecoating gun for emulating coating behavior of the coating gun; c)receiving training data of a plurality of robotic programs forindustrial coating and of their corresponding coating thickness coverageon a plurality of surfaces; d) processing the training data for machinelearning purposes to obtain first data tuples x and second data tuplesy, wherein x tuples are describing a point sequence on the surface, thecorresponding coating thickness coverage and specific information on therobot and on the surface, wherein y tuples are describing acorresponding robotic program; e) learning from the training dataprocessed, a function mapping the x tuples into the y tuples to generatea coating prediction module for the robot; f) for a given point sequenceof each given surface element, proceeding, starting with a givenselected valid thickness coverage as input parameters, in an iterativemanner to: i) predict the robotic program via the coating predictionmodule; ii) simulate a predicted robotic program with a collisiondetection engine within the industrial sub-cell; iii) calculatethickness values of the coating material on the given surface element bydetected collisions between elements of the coating dispersion objectmounted on the coating gun and sub-elements of the given surfaceelement; where the input parameters are iteratively tuned untilcalculated thickness values correspond to a set of desired value rangeand there are no unallowed robotic collisions within the industrialsub-cell; and g) generating a coating robotic program for each surfaceelement based on at least one said predicted robotic program resultingfrom the step f).
 2. The method according to claim 1, which comprisesselecting during at least one iteration: a different coating gun; adifferent brush; a different angle; and a different correspondingcoating dispersion object.
 3. The method according to claim 1, whichfurther comprises receiving inputs on the coating dispersion objectgenerated via a machine learning algorithm.
 4. A data processing system,comprising: a processor; an accessible memory connected to saidprocessor; the data processing system configured to: a) receive inputsincluding virtual representation of a robot, virtual representation of acoating gun, virtual representation of a set of elements of an objectsurface to be coated, a set of desired coating thickness ranges, andvirtual representation of an industrial sub-cell; b) receive inputs on acoating dispersion object to be mounted on the coating gun for emulatingcoating behavior of the coating gun; c) receive training data of aplurality of robotic programs for industrial coating and of theircorresponding coating thickness coverage on a plurality of surfaces; d)process the training data for machine learning purposes to obtain firstdata tuples x and second data tuples y; wherein x tuples are describinga point sequence on the object surface, the corresponding coatingthickness coverage and specific information on the robot and on theobject surface; wherein y tuples are describing a corresponding roboticprogram; e) learn from the training data processed a function mappingthe x tuples into the y tuples to generate a coating prediction modulefor the robot; f) for a given point sequence of each given surfaceelement, proceed, starting with a given selected valid thicknesscoverage as input parameters, in an iterative manner to: i) predict arobotic program via the coating prediction module; ii) simulate apredicted robotic program with a collision detection engine within theindustrial sub-cell; iii) calculate thickness values of a coatingmaterial on a given surface element by detected collisions betweenelements of the coating dispersion object mounted on the coating gun andsub-elements of the given surface element; where the input parametersare iteratively tuned until calculated thickness values correspond to aset of desired value ranges and there are no unallowed roboticcollisions within the industrial sub-cell; and g) generate a coatingrobotic program for each surface element based on at least one resultingpredicted robotic programs.
 5. The data processing system according toclaim 4, wherein the data processing system is configured to selectduring at least one iteration a different coating gun, brush and/orangle and to select a different corresponding coating dispersion object.6. The data processing system according to claim 4, wherein the dataprocessing system is configured to receive inputs on the coatingdispersion object generated via a machine learning algorithm.
 7. Anon-transitory computer-readable medium encoded with executable computerinstructions that, when executed, cause a data processing system to: a)receive inputs including virtual representation of a robot, virtualrepresentation of a coating gun, virtual representation of a set ofelements of an object surface to be coated, a set of desired coatingthickness ranges, and virtual representation of an industrial sub-cell;b) receive inputs on a coating dispersion object to be mounted on thecoating gun for emulating coating behavior of the coating gun; c)receive training data of a plurality of robotic programs for industrialcoating and of their corresponding coating thickness coverage on aplurality of surfaces; d) process the training data for machine learningpurposes to obtain first data tuples x and second data tuples y; whereinx tuples are describing a point sequence on a surface, the correspondingcoating thickness coverage and specific information on the robot and onthe surface; wherein the y tuples are describing the correspondingrobotic program; e) learn from the training data processed a functionmapping the x tuples into the y tuples to generate a coating predictionmodule for the robot; f) for a given point sequence of each givensurface element, proceed, starting with a given selected valid thicknesscoverage as input parameters, in an iterative manner to: i) predict arobotic program via the coating prediction module; ii) simulate apredicted robotic program with a collision detection engine within theindustrial sub-cell; iii) calculate thickness values of the coatingmaterial on the given surface element by detected collisions betweenelements of the coating dispersion object mounted on the coating gun andsub-elements of the given surface element; where the input parametersare iteratively tuned until calculated thickness values correspond to aset of desired value ranges and there are no unallowed roboticcollisions within the industrial sub-cell; and g) generate a coatingrobotic program for each surface element based on at least one resultingpredicted robotic programs.
 8. The non-transitory computer-readablemedium according to claim 7, wherein during at least one iteration adifferent coating gun, brush and/or angle is selected and a differentcorresponding coating dispersion object is selected.
 9. Thenon-transitory computer-readable medium according to claim 7, whereinreceived inputs on the coating dispersion object are generated via amachine learning algorithm.